🇨🇦Counterfactual Reasoning Using Bayesian Networks for Environmental Policy Analysis
Steven F. Wilson, Ph.D., EcoLogic Research
To be presented at the 2024 BayesiaLab Spring Conference in Cincinnati on April 12, 2024.
Abstract
Environmental policymaking is challenging because systems are complex, and rarely can we conduct experiments to test the relative costs and benefits of different policy options. Causal analysis methods allow us to estimate causal effects from observational data, and such methods are being applied increasingly often to predict the relative benefits of alternative policies. However, predictions based on only average causal effects provide an incomplete assessment of the value of potential interventions. Decision-makers also need to know how likely an outcome is to occur without the intervention (i.e., an assessment of causal attribution) or what outcomes could be expected if the intervention was only selectively applied (i.e., estimating context-specific causal effects). Answering these questions requires applying the counterfactual reasoning of “rung 3” of Pearl’s causal hierarchy. In fact, Pearl argued explicitly in his book Causality that “policy analysis is an exercise in counterfactual reasoning.” I used Bayesian Networks to model counterfactual outcomes on caribou populations of different land use policy interventions. While there are theoretical limitations to using Bayesian Networks for this purpose, the resulting counterfactual insights still provide additional value to decision-makers compared to observational or interventional analyses.
About the Presenter
Steven F. Wilson, Ph.D., EcoLogic Research, 302-99 Chapel Street, Nanaimo, BC V9R 5H3, Canada, steven.wilson@ecologicresearch.ca
Steve Wilson has 30 years of experience working at technical and professional levels in strategic and operational planning for wildlife and other ecological values. He specializes in quantitative approaches to decision support and policy analysis. Steve holds a Ph.D. in wildlife ecology from the University of British Columbia in Vancouver.
Register here for the 2024 BayesiaLab Spring Conference, April 11-12, 2024:
Previous Conference Presentations
🇨🇦pageA Causal Framework for Analyzing Cumulative Environmental Impacts🇨🇦pageLessons from Causal Analysis: Policy Implications for Woodland Caribou Recovery in Canada🇨🇦pageUsing Bayesian Networks to Map Winter Habitat for Mountain Goats in Coastal British Columbia🇨🇦pageThe Small Data Problem: Using Bayesian Networks in Endangered Species Policy Development🇨🇦pageUsing Bayesian Networks to Characterize Wildlife Habitat UseThe Social Graph—Using Bayesian Networks to Identify Spatial Population Structuring Among Caribou in Subarctic Canada (Paris, 2017)
Use of causal modeling with Bayesian networks to inform policy options for sustainable resource management (Nashville, 2016)
Big data, small data: Bayesian networks in environmental policy analysis in Canada’s energy sector (Fairfax, 2015)
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